Anomaly Detection · Schema

DetectionJob

Configuration for an anomaly detection job that analyzes one or more time series using a specified algorithm and detection settings.

Anomaly DetectionArtificial IntelligenceData ScienceFraud DetectionMachine LearningMonitoringObservabilityOutlier DetectionPattern RecognitionSecurityTime Series

Properties

Name	Type	Description
id	string	Unique identifier for the detection job.
name	string	Human-readable name for the detection job.
description	string	Description of what this detection job monitors and why.
status	string	Current operational status of the detection job.
algorithm	string	The anomaly detection algorithm used by this job.
mode	string	Detection mode — batch retrospective, streaming real-time, or multivariate correlation-based.
sensitivity	number	Sensitivity level controlling the anomaly detection threshold. Higher values detect more subtle anomalies.
seasonality	string	Seasonality pattern used for baseline modeling.
series_ids	array	List of time series identifiers analyzed by this job.
created_at	string	Timestamp when the job was created.
modified_at	string	Timestamp when the job was last modified.

View JSON Schema on GitHub

JSON Schema

{
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "$id": "https://raw.githubusercontent.com/api-evangelist/anomaly-detection/refs/heads/main/json-schema/anomaly-detection-detection-job-schema.json",
  "title": "DetectionJob",
  "description": "Configuration for an anomaly detection job that analyzes one or more time series using a specified algorithm and detection settings.",
  "type": "object",
  "properties": {
    "id": {
      "type": "string",
      "description": "Unique identifier for the detection job.",
      "example": "job-500456"
    },
    "name": {
      "type": "string",
      "description": "Human-readable name for the detection job.",
      "example": "Production API Latency Anomaly Detector"
    },
    "description": {
      "type": "string",
      "description": "Description of what this detection job monitors and why.",
      "example": "Monitors API p99 latency for anomalies using daily seasonality."
    },
    "status": {
      "type": "string",
      "enum": ["pending", "running", "paused", "closed", "failed"],
      "description": "Current operational status of the detection job.",
      "example": "running"
    },
    "algorithm": {
      "type": "string",
      "enum": ["basic", "agile", "robust", "iforest", "lof", "ocsvm", "autoencoder", "sr-cnn", "sarima", "graph-attention-network"],
      "description": "The anomaly detection algorithm used by this job.",
      "example": "agile"
    },
    "mode": {
      "type": "string",
      "enum": ["batch", "streaming", "multivariate"],
      "description": "Detection mode — batch retrospective, streaming real-time, or multivariate correlation-based.",
      "example": "streaming"
    },
    "sensitivity": {
      "type": "number",
      "minimum": 0,
      "maximum": 10,
      "description": "Sensitivity level controlling the anomaly detection threshold. Higher values detect more subtle anomalies.",
      "example": 3
    },
    "seasonality": {
      "type": "string",
      "enum": ["hourly", "daily", "weekly", "none", "auto"],
      "description": "Seasonality pattern used for baseline modeling.",
      "example": "daily"
    },
    "series_ids": {
      "type": "array",
      "description": "List of time series identifiers analyzed by this job.",
      "items": {
        "type": "string"
      },
      "example": ["ts-api-latency-p99", "ts-api-error-rate"]
    },
    "created_at": {
      "type": "string",
      "format": "date-time",
      "description": "Timestamp when the job was created.",
      "example": "2026-04-01T00:00:00Z"
    },
    "modified_at": {
      "type": "string",
      "format": "date-time",
      "description": "Timestamp when the job was last modified.",
      "example": "2026-04-19T00:00:00Z"
    }
  },
  "required": ["id", "name", "status", "algorithm", "mode"]
}